Sensing contact pressure applied by a gripper can benefit autonomous and teleoperated robotic manipulation, but adding tactile sensors to a gripper's surface can be difficult or impractical. If a gripper visibly deforms, contact pressure can be visually estimated using images from an external camera that observes the gripper. While researchers have demonstrated this capability in controlled laboratory settings, prior work has not addressed challenges associated with visual pressure estimation in the wild, where lighting, surfaces, and other factors vary widely. We present a model and associated methods that enable visual pressure estimation under widely varying conditions. Our model, Visual Pressure Estimation for Robots (ViPER), takes an image from an eye-in-hand camera as input and outputs an image representing the pressure applied by a soft gripper. Our key insight is that force/torque sensing can be used as a weak label to efficiently collect training data in settings where pressure measurements would be difficult to obtain. When trained on this weakly labeled data combined with fully labeled data that includes pressure measurements, ViPER outperforms prior methods, enables precision manipulation in cluttered settings, and provides accurate estimates for unseen conditions relevant to in-home use.

翻译：夹爪施加接触压力的感知可提升自主与远程操控机器人操作能力，但为夹爪表面附加触觉传感器往往困难或不切实际。当夹爪发生可见形变时，可通过外部摄像头观测图像实现接触压力的视觉估计。尽管研究人员已在受控实验环境中验证该能力，但先前工作未能解决非结构化环境中视觉压力估计面临的挑战——例如光照条件、表面材质等变量剧烈变化。我们提出一种模型及配套方法，使视觉压力估计能在变化剧烈的条件下稳定运行。该模型——机器人视觉压力估计模型（ViPER）——以眼在手摄像头图像为输入，输出表征柔性夹爪施加压力的图像。关键思路是利用力/力矩传感作为弱标签，在难以获取压力测量的环境中高效采集训练数据。当采用包含压力测量的全标注数据与弱标注数据联合训练时，ViPER性能超越先前方法，能在杂乱环境中实现精密操作，并对居家场景相关的未知条件提供精准估计。